Smart video surveillance system using a neural network engine

ABSTRACT

Remote neural network retraining for surveillance systems is disclosed. In some cases, systems and methods enable remote training and retraining of neural networks, while processing data in real life using the trained and retrained neural networks locally at the surveillance system. The surveillance system can determine a change of location and can retrain the neural network and/or initiate the retraining of the neural network remotely, such as on a cloud server, to retrain the neural network based on new image and/or video data taken from the new location. The remote server can transmit the retrained neural network and/or weights for nodes of the retrained neural network back to the surveillance system. The surveillance system can then update its neural network and process future image and/or video data based on the retrained neural network and/or weights.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 CFR 1.57.

TECHNICAL FIELD

The present disclosure relates to a neural network engine for asurveillance system, and more particularly, for remote neural networktraining for such engine.

BACKGROUND

Computer learning models can process large volumes of data. For example,a model may be implemented as an artificial neural network. Artificialneural networks are artificial in the sense that they are computationalentities, inspired by biological neural networks but modified forimplementation by computing devices. A neural network typicallycomprises an input layer, one or more hidden layer(s) and an outputlayer. The nodes in each layer connect to nodes in the subsequent layerand the strengths of these interconnections are typically learnt fromdata during the training process. Once trained, a neural network can beused for inference, that is, provided with new input data in order topredict the corresponding output.

Machine learning techniques, such as neural networks, are frequentlybeing utilized by modern computing systems. These technologies canoperate on large data sets and thus can require large amounts of storagespace. However, current memory architectures do not allow forscalability of big data analysis. The present disclosure addresses theseand other problems.

SUMMARY

The systems, methods, and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for theall of the desirable attributes disclosed herein.

A method of performing neural network computations in a surveillancesystem can comprise: storing a plurality of weights of a neural networkin a memory device for processing images or video from at least oneimage or video capture device; performing a first inference operationlocally at the surveillance system on data received from the at leastone image or video capture device without transmitting the received datato a remote server as part of the first inference operation; determininga change in location for the at least one image or video capture device;transmitting, to a remove server, first image or video data receivedfrom the at least one image or video capture device at the changedlocation; receiving, from the remote server, updated weights for theneural network responsive to the change in the location, the updatedweights reflective of retraining of the neural network based on thefirst image or video data at the changed location; receiving secondimage or video data from the at least one image or video capture deviceat the changed location; and performing a second inference operationlocally at the surveillance system on the second image or video data byprocessing the second image or video data in the neural network usingthe updated weights, wherein the method is performed by one or morehardware processors.

In the method of the preceding paragraph or any of the paragraphsherein, the first inference operation can be performed locally at thesurveillance system based on pre-configured weights.

In the method of the preceding paragraph or any of the paragraphsherein, determining the change in the location can be based on userinput.

In the method of the preceding paragraph or any of the paragraphsherein, determining the change in the location can be based on datareceived from a positioning system device of the surveillance system.

In the method of the preceding paragraph or any of the paragraphsherein, the change in the location can be automatically determined basedon a determination of a change in a background of the first image orvideo data.

In the method of the preceding paragraph or any of the paragraphsherein, retraining the neural network can be further based on anindication of a change in an inference operation type of the neuralnetwork.

In the method of the preceding paragraph or any of the paragraphsherein, the change of the inference operation type can include at leastone of: a change of object to be detected or a change of an angle viewof the at least one image or video capture device.

In the method of the preceding paragraph or any of the paragraphsherein, retraining the neural network can be based on a real-time videostream from the at least one image or video capture device.

In the method of the preceding paragraph or any of the paragraphsherein, retraining the neural network can be performed without anindication of an object present in the first image or video data.

A surveillance system can comprise one or more hardware processorsconfigured to: store a plurality of weights of a neural network in amemory device for processing data from at least one sensing device;perform a first inference operation on data received from the at leastone sensing device without transmitting the received data to a server aspart of the first inference operation; determine a change in locationfor the at least one sensing device; transmit, to a server, first senseddata received from the at least one sensing device at the changedlocation; receive, from the server, updated weights for the neuralnetwork responsive to the change in the location, the updated weightsreflective of re-training of the neural network based on the firstsensed data at the changed location; receive second sensed data from theat least one sensing device at the changed location; and perform asecond inference operation on the second sensed data by processing thesecond sensed data in the neural network using the updated weights.

In the system of the preceding paragraph or any of the paragraphsherein, the first sensed data can comprise image or video data.

In the system of the preceding paragraph or any of the paragraphsherein, the one or more hardware processors can be configured to store aplurality of preconfigured weights and the first inference operation isperformed based on those weights.

In the system of the preceding paragraph or any of the paragraphsherein, the at least one sensing device can be configured to transmitthe first sensed data to the one or more hardware processors viawireless communication.

In the system of the preceding paragraph or any of the paragraphsherein, the second inference operation can include identifying an age orgender of a human in the second sensed data.

In the system of the preceding paragraph or any of the paragraphsherein, the at least one sensing device can comprise at least one imageor video capture device and wherein the change in location includes atleast one of: a change in view angle of at least one of the at least oneimage or video capture device or a change in location outside of aprevious view for the at least one image or video capture device.

A system for performing neural network computations for surveillance cancomprise: means for storing a plurality of weights of a neural networkinto a memory device for processing images or video from at least oneimage or video capture device; means for determining a change inlocation for the at least one image or video capture device; means forretraining the neural network; means for loading updated weights for theneural network; means for receiving first image or video data from theat least one image or video capture device at the changed locationresponsive to the change in the location; and means for performing aninference operation on the first image or video data by processing thefirst image or video data in the neural network using the updatedweights.

In the system of the preceding paragraph or any of the paragraphsherein, the means for retraining the neural network can be further forretrieving weights for another preconfigured neural network stored inthe memory device.

In the system of the preceding paragraph or any of the paragraphsherein, the means for retraining the neural network can be further forretrieving weights for another preconfigured neural network stored in aremote server.

In the system of the preceding paragraph or any of the paragraphsherein, the means for retraining the neural network can be further forretraining based on user inputted data.

In the system of the preceding paragraph or any of the paragraphsherein, the system can further comprise: means for storing second imageor video data received from the at least one image or video capturedevice at the changed location, wherein means for retraining the neuralnetwork is further for generating the updated weights based on thesecond image or video data at the changed location.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of various inventive features will now be described withreference to the following drawings and appendices. Throughout thedrawings, reference numbers may be re-used to indicate correspondencebetween referenced elements. The drawings are provided to illustrateexample embodiments described herein and are not intended to limit thescope of the disclosure.

FIG. 1 depicts an illustration of a surveillance system in a driveway orgarage.

FIG. 2 depicts an illustration of a surveillance system in a livingroom.

FIG. 3 depicts an illustration of a surveillance system in a living roompositioned in a new view.

FIG. 4 depicts a flow diagram of retraining a neural network based on achange of location.

FIG. 5 depicts an illustration of a surveillance system with a neuralnetwork trained for a European style home.

FIG. 6 depicts an illustration of a surveillance system with a neuralnetwork trained for an Asian style home.

FIG. 7 depicts an illustration of a surveillance system with a neuralnetwork trained to extract specific attributes of persons.

FIG. 8 depicts a block diagram of preconfigured neural networks storedin the controller.

FIG. 9 depicts a block diagram of preconfigured neural networks storedin a local server.

DETAILED DESCRIPTION

While certain embodiments are described, these embodiments are presentedby way of example only, and are not intended to limit the scope ofprotection. Indeed, the novel methods and systems described herein maybe embodied in a variety of other forms. Furthermore, various omissions,substitutions, and changes in the form of the methods and systemsdescribed herein may be made without departing from the scope ofprotection.

The needs for surveillance systems are increasing, with increasedcapabilities of surveillance system being able to provide higher dataprocessing. Existing surveillance systems can connect to multiplecameras wirelessly, and the cameras can provide images and videos to acontroller of the surveillance systems wirelessly. One key change torecent surveillance systems is the introduction of artificialintelligence data processing to surveillance systems. These surveillancesystems can perform inference operations and other neural networkcomputations to analyze image and/or video data to provide importantinformation to the user, such as a prediction of a type of object thathas entered the scene.

One drawback to existing surveillance systems is that the surveillancesystems typically send the image and/or video data to a remote systemfor further data processing. The surveillance systems receive the imageand/or video data from image and/or video capture devices, such as acamera, and sends the image and/or video data to a cloud server. Thecloud server processes this data through their neural network and sendsback results of the data processing to the surveillance systems. Thedata throughput required to send the image and/or video data to theremote system is quite large, especially with the increased resolutioncapabilities of modern-day cameras. Moreover, because all image and/orvideo data is being sent to a remote server, there are potential privacyand internet security issues if there is a breach of data.

In other surveillance systems, the data processing can be performedlocally by the surveillance systems and not on a remote server. Adrawback to this approach is that the data processing is typically verysimple. Moreover, these surveillance systems typically do not retrainthe neural network for new circumstances, such as a change in location.Retraining the neural network typically requires high computationalpower, such as forward and backward propagating of data through theneural network using a training data set. Thus, these systems areusually limited to application in set scenarios and run only a fixedneural network that has already been preprogrammed.

Some systems and methods described herein mitigate and/or eliminate oneor more drawbacks for existing systems. The present disclosure includessystems and methods for enabling remote training and retraining ofneural networks, while processing data in real life using the trainedand retrained neural networks locally at the surveillance system.

In some cases, a surveillance system can include a controller and one ormore sensing devices, such as video and/or image capture devices. Thesensing devices can use sensors to capture data, such as data related toits surroundings. For example, the one or more video and/or imagecapture devices can capture image and/or video data and send the imageand/or video data to a controller. The image and/or video data can betransmitted wirelessly from the one or more image and/or video capturedevices to the controller. In some embodiments, the image and/or videodata can be transmitted via wired communication.

In some cases, the controller can process the video and/or image datavia a neural network in order to perform inference operations. Theneural network can perform object detection on the data. For example,the neural network can determine a particular type of car entered a carparking lot.

In some cases, the surveillance system can determine a change oflocation for at least one of the image and/or video capture devices. Thesurveillance system can receive an indication of a change of locationfrom a user or customer. In some cases, the surveillance system canautomatically determine a change of location by performing inferenceoperations on the video and/or image data, such as identifying differentfurniture that was previously identified in the image. In some cases,the surveillance system can determine a change of location based onlocational information, such as a positioning system, a globalpositioning system (GPS), or other system that can provide locationalinformation.

In some cases, the surveillance system can retrain the neural networkand/or initiate the retraining of the neural network. The retraining canbe based upon a consumer request, a consumer indication of a change ofan inference operation type (such as changing from car identification toperson identification), or from a change of location. The image and/orvideo data can be transmitted to a remote server, such as a cloudserver, to retrain the neural network based on new image and/or videodata taken from the new location.

In some cases, the remote server can transmit the retrained neuralnetwork and/or weights for nodes of the retrained neural network back tothe surveillance system. The surveillance system can then update itsneural network and process future image and/or video data based on theretrained neural network and/or weights. Advantageously, a subset of theimage and/or video data needs to be transmitted remotely. Moreover, dataprocessing can be custom tailored to the new location. Because dataprocessing for future image and/or video data on the retrained neuralnetwork is performed locally, the data is more secure, less internettraffic is required, and the surveillance system can be retrained forany new scenario.

Surveillance System in Various Scenarios

FIG. 1 depicts an illustration 100 of a surveillance system in adriveway or garage. In the illustration 100, the surveillance system caninclude one or more image and/or video capture devices, such as camera104 installed in the garage 102. The surveillance system can include acontroller 108, such as a local processor, and a memory device 106, suchas a memory chip, drive, or the like.

In some cases, the surveillance system of illustration 100 can include aneural network stored in the memory device 106, and can be retrieved andexecuted by the controller 108. The neural network of the surveillancesystem of illustration 100 can be trained to perform inferenceoperations on image and/or video data captured by the one or more imageand/or video capture devices, such as camera 104. The camera 104 cantake a video of the garage and/or images at certain intervals. Thecamera 104 can wirelessly transmit such data to the controller 108 to bestored in the memory device 106.

In some cases, the controller 108 of the surveillance system ofillustration 100 can process the image and/or video data stored in thememory device 106. The controller 108 can retrieve the image and/orvideo data from the memory device 106 and the neural network from thememory device 106. The controller 108 can perform inference operationsby inputting the image and/or vide data into the neural network. Theneural network can assess the image and/or video data to determinewhether a certain object is present. For example, the neural network canbe trained to identify that a car 110 or a bike 112 has entered thegarage.

In some cases, the neural network can be trained to perform one or morefunctions. For example, the neural network can be trained to identify anobject type, such as to differentiate between a car 110 or a bike 112.The neural network can perform more detailed inferences, such as toidentify a type of car, or certain characteristics of the car, such as acar's spoiler or rims. The neural network can be trained to output asingle determination of a characteristic, such as the highestprobability of a car type. The neural network can be trained to output aplurality of characteristics, such as various characteristics of theidentified car.

In some cases, the neural network can be trained for a particularlocation. For example, the neural network can be trained for the garage102. The neural network can identify certain objects that are typicallypresent in the garage 102, such as a car 110 or a bike 112.

FIG. 2 depicts an illustration 200 of a surveillance system in a livingroom 202. The surveillance system can be moved from the garage 102 ofFIG. 1 to the living room 202 of FIG. 2.

In some cases, a change of location results in a change of positioningof the camera 104 of FIG. 1. For example, the camera 104 of FIG. 1 canbe moved to a new position, such as the position shown of camera 204 ofFIG. 2.

In some cases, the change of location can be determined by thesurveillance system manually, such as based on a user's input of thechange in location. The user can indicate the changes, such as thechange to a living room, and/or changes to desired inference operations,such as to identify persons or a certain type of pet, such as a dog 210.

In some cases, the change of location can be automatically determined bythe surveillance system. For example, the change of location can bedetermined by a change in a global positioning system. The globalpositioning system can be within a controller 206 of the surveillancesystem. The global positioning system can be installed in the one ormore image and/or video capture devices, such as the camera 204 of FIG.2.

In some cases, an image and/or video capture device, such as camera 204,can include an inertial navigation system. The camera 204 can determinethat the camera 204 is oriented in a certain XYZ direction based oninput from an inertial navigation system. As such, the camera 204 candetermine the angle of the view for the living room 202.

In some cases, the change of location can be determined by the imageand/or video data taken by the camera 204. For example, image and/orvideo data can be captured and transmitted by the camera 204. Thecontroller 206 can receive the image and/or video data, retrieve aneural network for the garage 102 of FIG. 1, process the image and/orvideo data through the neural network, and determine that the objectsdetected in the garage 102 is no longer present in the living room, andthat the living room includes other new objects that are typically notin the garage 102.

In some cases, based on a change of location, the surveillance systemcan determine a need to retrain the neural network, such as to retrainthe neural network for the living room 202. The controller 206 cantransmit the captured image and/or video data to a remote sever (notshown) for retraining.

In some cases, the remote server can retrain the neural network for theliving room. The remote server can send the retrained neural network(and/or the weights for nodes of the retrained neural network) back tothe controller 206. The controller 206 can store the retrained neuralnetwork into the memory device 208.

In some cases, with the retrained the neural network, the controller 206can process future image and/or video data captured by the image and/orvideo capture devices, such as camera 204, locally and/or in real-time.The camera 204 can capture new image and/or video data, and thecontroller 206 now can identify that a dog 210 is present in a livingroom 202 based on processing the new image and/or video data through theretrained neural network.

FIG. 3 depicts an illustration 300 of a surveillance system in a livingroom positioned in a new view. The camera 204 of FIG. 2 can be relocatedwithin the same living room 202 of FIG. 2 to a new location for thecamera 304 in FIG. 3.

In some cases, the surveillance system can identify a change oflocation, such as a change of location for one or more cameras. Thesurveillance system can receive image and/or video data from the camera304 and can identify that the background of the living room has changed.The surveillance system can determine that the location has changed.

In some cases, the surveillance system can determine that although thebackground has changed, that the camera 304 is still in the same livingroom 302 as the living room 202 of FIG. 2. The surveillance system candetermine that certain background features that were identified from theimage and/or video data captured by the camera 304 in the new locationare the same background features as the image and/or video data capturedby the camera 204 of FIG. 2 in the previous location. For example, thecamera 304 can determine that the chair 312A, the lamp 312B, fireplace312C, and the couch 312D are present in the image and/or video data butin a different location.

In some cases, the surveillance system can use the same neural networkfor the position in FIG. 2 as in FIG. 3. In some cases, the surveillancesystem can retrain the neural network for the new view within the sameliving room 302. The surveillance system can capture new image and/orvideo data captured in the new view from camera 304. The new imageand/or video data can be transmitted to a remote server (not shown) forretraining. The weights of the neural network can be newly generatedand/or updated based on the new image and/or video data. The remoteserver can transmit the new weights back to the controller 308 andstored in the memory device 306. Then, the surveillance system canperform inference operations based on the camera 304 capturing imageand/or video data in the new view.

Remote Retraining of the Neural Network

FIG. 4 depicts a flow diagram of a process 400 of retraining a neuralnetwork based on a change of location. The process 400 can beimplemented by any system that can capture image and/or video data. Forexample, the process 400, in whole or in part, can be implemented by thesurveillance system, a remote server, or other computing system.Although any number of systems, in whole or in part, can implement theprocess 400, to simplify discussion, the process 400 will be describedwith respect to particular systems. Further, although cases of theprocess 400 may be performed with respect to variations of systemscomprising neural network engines, to simplify discussion, the process400, will be described with respect to the image and/or video capturedevice, the controller, and a machine learning algorithm trainingserver, such as a remote server.

At block 406, the process 400 can load a preconfigured machine learningalgorithm into a memory device. For example, the process 400 can loadthe preconfigured machine learning algorithm, such as a neural networkfor the garage of FIG. 1.

At block 408, the process 400 can determine a change of location orobject of interest for the surveillance system. The change of locationor object of interest can be based on an inference operation performedlocally at the surveillance system and based on data received by theimage or video capture device without transmitting the received data toa remote server as part of the inference operation. The process 400 candetermine a change of location based on a user input, a change inlocation data from a global positioning system, and/or a change inbackground data from image and/or video captured by the image and/orvideo capture devices. For example, the surveillance system can be movedto the living room 202 of FIG. 2. The process 400 can determine that theneural network has to be retrained to perform inference operations atthe living room. In some embodiments, the change can include a viewchange, such as an angle change in view.

In some cases, a change of object of interest can include a change ofobject type, such as identifying a cat instead of a dog, or a bikeinstead of a car. In some cases, the change of object can be basedautomatically on an unidentifiable object entering into a scene. In somecases, the change of object can be based on user input, such as a userrequesting inference operations for poodles instead of goldenretrievers.

At block 402, the image and/or video capture device can capture firstimage and/or video data to be used for the retraining. The image and/orvideo capture device can transmit the first image and/or video data tothe controller.

At block 410, the process 400 can receive the first image and/or videodata from the image and/or video capture device, and at block 412, thecontroller can transmit the first image and/or video data to a machinelearning algorithm training server, such as a remote server and/or acloud server.

At block 418, the remote server can receive the first image and/or videodata taken from the new location and retrain a neural network.Retraining the neural network can include updating weights for nodes ofan existing neural network. The weights of the neural network for theprevious location can be updated. In other cases, the server canretrieve a neural network for the type of location for the new location,such as retrieving a preexisting neural network for a living room. Theserver can then use the first image and/or video data to retrain thepreexisting neural network for the living room. Responsive to the neuralnetwork being trained, the remote server can transmit the retrainedneural network (e.g., the machine learning algorithm) to the controller.

In some cases, the first image and/or video data is stored on the cloud,such as the remote cloud server.

At block 414, the process 400 can receive the retrained neural networkfrom the remote server and store the retrained neural network into amemory device.

At block 404, the image and/or video capture device can capture newimage and/or video data, such as second image and/or video data, and atblock 416, the controller can perform inference operations on the secondimage and/or video data using the retrained neural network locally.Advantageously, sensitive image and/or video data need only to betransmitted to the remote server for training, and future inferenceoperations can be performed locally. Moreover, the data passed betweenthe controller of the surveillance system and an external remote serveris greatly reduced, resulting in reduced network throughputrequirements. Furthermore, because the inferences are performed locallyat block 416 using a neural network retrained for the new location, theinference operations are more accurate resulting in higher correctdetections and reduced false alarms. Moreover, the processing times ofthe neural networks are not dependent on network latency. Thus,inference operations can be performed in more real-time, and thesurveillance systems responsive to real-time streams of image and/orvideo data.

Surveillance System Retrained for Certain Styles

FIG. 5 depicts an illustration 500 of a surveillance system with aneural network trained for a European style home. The surveillancesystem can be trained to perform inference operations for one or morestyles. For example, in the illustration 500, the surveillance systemcan be installed in a living room in Europe. The neural network of thesurveillance system can be trained to identify furniture typical in aEuropean living room 502. The image and/or video capture device 504 cancapture image and/or video data of the European living room 502. Thesurveillance system can send the image and/or video data to a remoteserver, and the remote server can transmit a retrained neural networkback to the surveillance system. For example, the remote server canidentify that the European living room 502 includes furniture that areof European style, such as the couches 506A, 506B, 506C, 506D, the rug510, and the dish cabinet 508.

FIG. 6 depicts an illustration 600 of a surveillance system with aneural network trained for an Asian style home. The surveillance systemcan determine a change of location, as described herein.

In some cases, the image and/or video capture device 504 can captureimage and/or video data of the living room 502 and send the data to acontroller. The controller can transmit the image and/or video data ofthe living room 502 to a remote server for retraining the neural networkfor the Asian style home. The remote server can send the retrainedneural network back to the controller, and thereafter, the controllercan perform inference operations specific to the Asian style living room502. The controller can receive image and/or video data of the livingroom 502 and the retrained neural network can detect objects typical foran Asian style living room 502, such as a wall ornament 608A, 608B,couches 604A, 604B, a coffee table 606, and a rug 612.

FIG. 7 depicts an illustration 700 of a surveillance system with aneural network trained to extract specific attributes of persons. Thesurveillance system can send image and/or video data captured by theimage and/or video capture device 704 to a remote server. The remoteserver can retrain the neural network to identify persons andcorresponding characteristics. For example, the remote server can trainthe neural network to identify that a person 710 is a female, wearing acertain type of glasses 712, a brand or style of dress 714, and a typeof shoes 716. The remote server can transmit the retrained neuralnetwork to the controller 706, and the controller 706 can store theretrained neural network into a memory device 708.

Preconfigured Neural Networks Stored in Controller

FIG. 8 depicts a block diagram 800 of preconfigured neural networksstored in the controller. The surveillance system of block diagram 800can include a video and/or image capture device 818, and a controller802. The controller 802 can include a neural network processor 816, andone or more databases to store the neural network. The one or moredatabases can include neural networks for certain locations, styles,and/or object types. For example, the one or more databases can includea car neural network(s) 804, a pet neural network(s) 810, a living roomneural network(s) 806, a parking lot neural network(s) 812, a Chinaneural network(s) 808, a European neural network(s) 814, and/or thelike.

In some cases, the surveillance system captures image and/or video datafrom the video and/or image capture device 818. The controller receivesthe image and/or video data and can determine the most familiar scene,object, or style based on the image and/or video data. For example, thecontroller can process the image and/or video data through an imageand/or video data processor, such as a neural network for generalidentification of objects, styles, or locations. Based on anidentification of a certain general object, such as a car and a pet, thecontroller 802 can retrieve the car neural network(s) 804 and/or a petneural network(s) 810 for more accurate detection of cars or pets. Basedon a determination of a location type such as a living room or a parkinglot, the controller 802 can retrieve the living room neural network(s)806 or the parking lot neural network(s) 812. Based on a determinationof a certain style (e.g., from user input, locational data, oridentification of certain objects in the image and/or video data), thesurveillance system can retrieve a China neural network(s) 808 or aEurope neural network(s) 814.

In some cases, the preconfigured neural networks can be retrieved by thecontroller with preloaded weights and models. In the case of FIG. 8, thepreconfigured neural networks are stored locally at the controller 802.

In some cases, the preconfigured neural networks can be stored in amemory device within the controller. In some cases, the preconfiguredneural networks can be stored in a memory device external to thecontroller but located in the same location as the surveillance system.

Preconfigured Neural Networks Stored in Local Server

FIG. 9 depicts a block diagram 900 of preconfigured neural networksstored in a local server. The surveillance system of block diagram 900can include a video and/or image capture device 920, a controller 918,and a local server 902. The local server 902 can include a neuralnetwork trainer 916, and one or more databases to store the neuralnetwork. The one or more databases can include neural networks forcertain locations, styles, and/or object types. For example, the one ormore databases can include a car neural network(s) 904, a pet neuralnetwork(s) 910, a living room neural network(s) 906, a parking lotneural network(s) 912, a China neural network(s) 908, a European neuralnetwork(s) 914, and/or the like. The controller 918 can include a neuralnetwork processor 922.

In some cases, the surveillance system can retrieve video and/or imagedata from the video and/or image capture device 920. The surveillancesystem can transmit the video and/or image data to the controller 918and thereafter to the local server 902. The controller can indicate aneed to retrain the neural network to the local server 902.

In some cases, the local server 902 can retrieve preloaded neuralnetworks, such as a car neural network(s) 904, a pet neural network(s)910, a living room neural network(s) 906, a parking lot neuralnetwork(s) 912, a China neural network(s) 908, a European neuralnetwork(s) 914, and/or the like based on a certain indication of anobject, style, or location (such as from locational data of a globalpositioning system). The neural network trainer 916 can retrain theneural networks specific to the image and/or video data captured by thevideo and/or image capture device 920. The local server 902 can transmitthe retrained neural network back to the controller 918, and thecontroller can perform inference operations via its neural networkprocessor 922.

Other Variations

Those skilled in the art will appreciate that in some cases additionalsystem components can be utilized, and disclosed system components canbe combined or omitted. Although some embodiments describe video datatransmission, disclosed systems and methods can be used for transmissionof any type of data. The actual steps taken in the disclosed processesmay differ from those shown in the figures. Depending on the embodiment,certain of the steps described above may be removed, others may beadded. Accordingly, the scope of the present disclosure is intended tobe defined only by reference to the appended claims.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the protection. Indeed, the novel methods and systems describedherein may be embodied in a variety of other forms. Furthermore, variousomissions, substitutions and changes in the form of the methods andsystems described herein may be made without departing from the spiritof the protection. The accompanying claims and their equivalents areintended to cover such forms or modifications as would fall within thescope and spirit of the protection. For example, the systems and methodsdisclosed herein can be applied to hard disk drives, hybrid hard drives,and the like. In addition, other forms of storage (such as, DRAM orSRAM, battery backed-up volatile DRAM or SRAM devices, EPROM, EEPROMmemory, etc.) may additionally or alternatively be used. As anotherexample, the various components illustrated in the figures may beimplemented as software and/or firmware on a processor, ASIC/FPGA, ordedicated hardware. Also, the features and attributes of the specificembodiments disclosed above may be combined in different ways to formadditional embodiments, all of which fall within the scope of thepresent disclosure.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will further be understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Further, references to “a method” or“an embodiment” throughout are not intended to mean the same method orsame embodiment, unless the context clearly indicates otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the various embodiments of the present disclosure hasbeen presented for purposes of illustration and description, but is notintended to be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thisdisclosure. The example embodiments were chosen and described in orderto best explain the principles of this disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand this disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

Although the present disclosure provides certain preferred embodimentsand applications, other embodiments that are apparent to those ofordinary skill in the art, including embodiments which do not provideall of the features and advantages set forth herein, are also within thescope of this disclosure. Accordingly, the scope of the presentdisclosure is intended to be defined only by reference to the appendedclaims.

What is claimed is:
 1. A method of performing neural networkcomputations in a surveillance system, the method comprising: storing aplurality of weights of a neural network in a memory device forprocessing images or video from at least one image or video capturedevice; performing a first inference operation locally at thesurveillance system on data received from the at least one image orvideo capture device without transmitting the received data to a remoteserver as part of the first inference operation; determining a change inlocation for the at least one image or video capture device;transmitting, to a remove server, first image or video data receivedfrom the at least one image or video capture device at the changedlocation; receiving, from the remote server, updated weights for theneural network responsive to the change in the location, the updatedweights reflective of retraining of the neural network based on thefirst image or video data at the changed location; receiving secondimage or video data from the at least one image or video capture deviceat the changed location; and performing a second inference operationlocally at the surveillance system on the second image or video data byprocessing the second image or video data in the neural network usingthe updated weights, wherein the method is performed by one or morehardware processors.
 2. The method of claim 1, wherein the firstinference operation is performed locally at the surveillance systembased on pre-configured weights.
 3. The method of claim 1, whereindetermining the change in the location is based on user input.
 4. Themethod of claim 1, wherein determining the change in the location isbased on data received from a positioning system device of thesurveillance system.
 5. The method of claim 1, wherein the change in thelocation is automatically determined based on a determination of achange in a background of the first image or video data.
 6. The methodof claim 1, wherein retraining the neural network is further based on anindication of a change in an inference operation type of the neuralnetwork.
 7. The method of claim 6, wherein the change of the inferenceoperation type includes at least one of: a change of object to bedetected or a change of an angle view of the at least one image or videocapture device.
 8. The method of claim 1, wherein retraining the neuralnetwork is based on a real-time video stream from the at least one imageor video capture device.
 9. The method of claim 1, wherein retrainingthe neural network is performed without an indication of an objectpresent in the first image or video data.
 10. A surveillance system, thesystem comprising one or more hardware processors configured to: store aplurality of weights of a neural network in a memory device forprocessing data from at least one sensing device; perform a firstinference operation on data received from the at least one sensingdevice without transmitting the received data to a server as part of thefirst inference operation; determine a change in location for the atleast one sensing device; transmit, to a server, first sensed datareceived from the at least one sensing device at the changed location;receive, from the server, updated weights for the neural networkresponsive to the change in the location, the updated weights reflectiveof re-training of the neural network based on the first sensed data atthe changed location; receive second sensed data from the at least onesensing device at the changed location; and perform a second inferenceoperation on the second sensed data by processing the second sensed datain the neural network using the updated weights.
 11. The system of claim10, wherein the first sensed data comprises image or video data.
 12. Thesystem of claim 10, wherein the one or more hardware processors areconfigured to store a plurality of preconfigured weights and the firstinference operation is performed based on those weights.
 13. The systemof claim 10, wherein the at least one sensing device is configured totransmit the first sensed data to the one or more hardware processorsvia wireless communication.
 14. The system of claim 10, wherein thesecond inference operation includes identifying an age or gender of ahuman in the second sensed data.
 15. The system of claim 10, wherein theat least one sensing device comprises at least one image or videocapture device and wherein the change in location includes at least oneof: a change in view angle of at least one of the at least one image orvideo capture device or a change in location outside of a previous viewfor the at least one image or video capture device.
 16. A system forperforming neural network computations for surveillance, the systemcomprising: means for storing a plurality of weights of a neural networkinto a memory device for processing images or video from at least oneimage or video capture device; means for determining a change inlocation for the at least one image or video capture device; means forretraining the neural network; means for loading updated weights for theneural network; means for receiving first image or video data from theat least one image or video capture device at the changed locationresponsive to the change in the location; and means for performing aninference operation on the first image or video data by processing thefirst image or video data in the neural network using the updatedweights.
 17. The system of claim 16, wherein the means for retrainingthe neural network is further for retrieving weights for anotherpreconfigured neural network stored in the memory device.
 18. The systemof claim 16, wherein the means for retraining the neural network isfurther for retrieving weights for another preconfigured neural networkstored in a remote server.
 19. The system of claim 16, wherein the meansfor retraining the neural network is further for retraining based onuser inputted data.
 20. The system of claim 16, wherein the systemfurther comprises: means for storing second image or video data receivedfrom the at least one image or video capture device at the changedlocation, wherein means for retraining the neural network is further forgenerating the updated weights based on the second image or video dataat the changed location.